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Insights into relevant knowledge extraction techniques: a comprehensive review
journal contributionposted on 2020-01-01, 00:00 authored by A Shahid, M T Afzal, Moloud Abdar, M E Basiri, X Zhou, N Y Yen, J W Chang
More than 50 million journal papers will have been published by the end of 2019 with 2 million more journal papers published every year. The number of conference papers is even higher, and millions of other types of scientific research are added to the knowledge base every year. Scientific databases such as Web of Science, Scopus, and PubMed index millions of scientific papers and Google Scholar indexes a huge amount of scientific knowledge across diverse domains. However, current systems provide long lists of results when users attempt to find relevant papers, leaving them with little choice other than manually skimming through the lists. This article surveys different techniques used to identify relevant research papers by knowledge-based organizations. We categorized current literature content as content, metadata, collaborative filtering, and citation based techniques and identified the strengths and limitation for each approach. Further, we evaluated the published techniques and research-based products used to identify relevant documents and identified the strengths and limitations of each approach. This research will greatly help to understand current state-of-the-art techniques internal workings for finding relevant papers, understand the relevant strengths and limitations, and explore previously proposed techniques targeting this area.
JournalJournal of supercomputing
Pagination1695 - 1733
LocationNew York, N.Y.
Publication classificationC1 Refereed article in a scholarly journal
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Science & TechnologyTechnologyComputer Science, Hardware & ArchitectureComputer Science, Theory & MethodsEngineering, Electrical & ElectronicComputer ScienceEngineeringScientific big dataPaper related repositoryCitation analysisCollaborative filteringContent analysisMetadata analysisSCIENTIFIC LITERATURESCIENCECLASSIFICATIONRECOMMENDATIONARTICLESIMPROVE